JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2014, Vol. 44 ›› Issue (6): 19-25.doi: 10.6040/j.issn.1672-3961.1.2014.180

Previous Articles     Next Articles

A prediction method of atmospheric PM2.5 based on DBNs

ZHENG Yi, ZHU Chengzhang   

  1. College of Computer Science, National University of Defense Technology, Changsha 410073, Hunan, China
  • Received:2014-01-23 Revised:2014-10-27 Online:2014-12-20 Published:2014-01-23

PDF (PC)

2668

Abstract: A DBNs-based (deep belief networks) method for forecasting the daily average concentrations of PM2.5 in Xian was proposed. Besides, the way to select training data set as well as the DBNs parameters was optimized. Then relative experiments and comparison with methods based on BP (back propagation) and RBF (radial basis function) artificial neural network confirmed the feasibility and precision of DBNs. The results showed that the MSE (mean square error) between DBNs simulated PM2.5 daily average concentrations and observed ones was 8.47×10-4 mg2/m6, while the MSE of RBF and BP was 1.30×10-3 mg2/m6 and 1.96×10-3 mg2/m6 respectively. Therefore the DBNs-based method was fit for prediction of PM2.5 concentrations and it predicted more accurately than those methods based on RBF and BP artificial neural network.

Key words: restricted boltzmann machine, deep belief networks, PM2.5 prediction, deep learning, machine learning

CLC Number: 

  • TU457
[1] 郭晓泽,单思行.针对PM2.5的综述[J].能源与节能,2012(11):58-59. GUO Xiaoze, SHAN Sixing. Overview of PM2.5[J]. Energyand Energy Conservation, 2012(11):58-59.
[2] 周蕊,邵帅,王彦清.PM2.5的毒性及机制的进展[J].科技传播,2012(6):100. ZHOU Xin, SHAO Shuai, WANG Yanqing. Progress of PM2.5 toxicity and mechanisms[J]. Science & Technology Information, 2012(6):100.
[3] 李恩敬,艾春艳,赵飞.基于文献计量的PM2.5国内外研究情况分析[J].环境与可持续发展,2013,38(4):34-37. LI Enjing, AI Chunyan, ZHAO Fei. Study on PM2.5 baseon papers[J]. Environment and Sustainable Development, 2013, 38(4):34-37.
[4] WANG W, GUO Y. Air pollution PM2.5 data analysis in Los Angeles long beach with seasonal ARIMA model[C]//Proceedings of International Conference on Energy and Environment Technology. Guiling: IEEE, 2009, 3:7-10.
[5] MCKENDRY I G. Evaluation of artificial neural networks for fine particulate pollution (PM10 and PM2.5) forecasting[J]. Journal of the Air & Waste Management Association, 2002, 52(9):1096-1101.
[6] ABRAHAM A. Artificial neural networks[J].Handbook of Measuring System Design, 2005, 129(2):901-908.
[7] YAO L, LU N, JIANG S. Artificial neural network (ANN) for multi-source PM2.5 estimation using surface, MODIS, and meteorological data[C]//Proceedings of International Conference on Biomedical Engineering and Biotechnology. Macau: IEEE, 2012:1228-1231.
[8] ZHU C L, JIANG Z F, WANG Q. Forecastingmodel of environment air quality based on BP neural network[J]. JisuanjiGongcheng yu Yingyong (Computer Engineering and Applications), 2007, 42(22):223-227.
[9] JIANG Z, MENG X, YANG C, et al. A BP neural network prediction model of the urban air quality based on rough set[C]//Proceedings of Fourth International Conference on Natural Computation. Jinan: IEEE, 2008, 1:362-370.
[10] ZHENG H M, SHANG X X. Study on prediction of atmospheric PM2.5 based on RBF neural network[C]//Proceedings of Fourth International Conference on Digital Manufacturing and Automation. Hefei: IEEE, 2013:1287-1289.
[11] REHMAN M Z, NAWI N M. The effect of adaptive momentum in improving the accuracy of gradient descent back propagation algorithm on classification problems[C]//Proceedings of Software Engineering and Computer Systems. Berlin:Springer, 2011:380-390.
[12] 王敏,邹滨,郭宇,等.基于BP人工神经网络的城市PM2.5浓度空间预测[J]. 环境污染与防治, 2013,35(9):63-66. WANG Min, ZHOU Bin, GUO Yu, et al. BP artificialneural network-based analysis of spatial variability of urban PM2.5 concentration[J]. Environmental Pollution & Control, 2013, 35(9):63-66.
[13] 陈一萍,郑朝洪.BP和RBF网络在厦门市大气环境质量评价中的比较[J].环保科技,2008,14(4):8-11. CHEN Yiping, ZHENG Chaohong. Comparison of environmental quality evaluation based on BP and RBF network in Xiamen[J]. Environmental Protection and Technology, 2008, 14(4):8-11.
[14] 刘妹琴,廖晓昕.RBF神经网络的一种鲁棒学习算法[J].华中理工大学学报,2000,28(2):8-10. LIU Meiqin, LIAO Xiaoxi. A robust RBF neural network learning algorithm[J]. Journal of Huazhong University of Science and Technology, 2000, 28(2):8-10.
[15] 孙志军,薛磊,许阳明,等.深度学习研究综述[J].计算机应用研究,2012,29(8):2806-2810. SUN Zhijun, XUE Lei, XU Yangming, et al. Overview ofdeep learning[J]. Application Research of Computers, 2012, 29(8):2806-2810.
[16] DJURDJEVIC P D, HUBER M. Deep belief network for modeling hierarchical reinforcement learning policies[C]//Proceedings of 2013 International Conference on Systems, Man, and Cybernetics. Manchester: IEEE, 2013:2485-2491.
[17] FOUSEK P, RENNIE S, DOGNIN P, et al. Direct product based deep belief networks for automatic speech recognition[C]//Proceedings of 2013 International Conference on Acoustics, Speech and Signal Processing. Vancouver: IEEE, 2013:3148-3152.
[18] ZHOU S, CHEN Q, WANG X. Discriminative deep belief networks for image classification[C]//Proceedings of 17th International Conference on Image Processing. Las Vegas: IEEE, 2010:1561-1564.
[19] GHAHABI O, HERNANDO J. Deep belief networks for i-vector based speaker recognition[C]//Proceedings of 2014 International Conference on Acoustics,Speechand Signal Processing. Florence: IEEE, 2014:1700-1704.
[20] RIOUX L, GIGUERE P. Sign language fingerspelling classification from depth and color images using a deep belief network[C]//Proceedings of 2014 Canadian Conference on Computer and Robot Vision. Quebec: IEEE, 2014:92-97.
[21] ZHU C C, YIN J P, LI Q. A stock decision support system based on DBNs[J].Journal of Computational Information Systems, 2014, 10(2):883-893.
[1] ZHANG Mian, HUANG Ying, MEI Haiyi, GUO Yu. Intelligent interaction method for power distribution robot based on Kinect [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(5): 103-108.
[2] XIE Zhifeng, WU Jiaping, MA Lizhuang. Chinese financial news classification method based on convolutional neural network [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 34-39.
[3] HE Zhengyi, ZENG Xianhua, GUO Jiang. An ensemble method with convolutional neural network and deep belief network for gait recognition and simulation [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(3): 88-95.
[4] TANG Leshuang, TIAN Guohui, HUANG Bin. An object fusion recognition algorithm based on DSmT [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(1): 50-56.
[5] ZHOU Funa, GAO Yulin, WANG Jiayu, WEN Chenglin. Early diagnosis and life prognosis for slowlyvarying fault based on deep learning [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 30-37.
[6] LIU Yang, LIU Bo, WANG Feng. Optimization algorithm for big data mining based on parameter server framework [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(4): 1-6.
[7] WEI Bo, ZHANG Wensheng, LI Yuanxiang, XIA Xuewen, LYU Jingqin. A sparse online learning algorithm for feature selection [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(1): 22-27.
[8] HE Zhengyi, ZENG Xianhua, QU Shengwei, WU Zhilong. The time series prediction model based on integrated deep learning [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(6): 40-47.
[9] ZHOU Wang, ZHANG Chenlin, WU Jianxin. Qualitative balanced clustering algorithm based on Hartigan-Wong and Lloyd [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(5): 37-44.
[10] MENG Lingheng, DING Shifei. Depth perceptual model based on the single image [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(3): 37-43.
[11] LIU Jie, YANG Peng, LYU Wensheng, LIU Agudamu, LIU Junxiu. Prediction models of PM2.5 mass concentration based on meteorological factors [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(6): 76-83.
[12] XIE Lin1, YIN Xi-yao2, LI Fan-zhang3, WU Jia3. A kind of inverse resolution learning expression [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2013, 43(4): 46-50.
[13] HE Xue-ying1, 2, QIN Wei1, YIN Yi-long1 *, ZHAO Lian-zheng1，QIAO Hao3. Video-based fingerprint verification using machine learning [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2011, 41(4): 29-33.
[14] LIANG Chun-lin1, PENG Ling-xi2*. An immune network based unsupervised classifier [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2010, 40(5): 82-86.
[15] GUO Mao-Zu, ZOU Quan, LI Wen-Bin, HAN Ying-Peng. Learning in bioinformatics [J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(3): 1-6.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of Journal of Shandong University (Engineering Science)
Supported by Beijing Magtech Co.Ltd